Textual Clustering Analysis of River Chief System Policy in China

DOI

10.2991/978-94-6463-064-0_17How to use a DOI?

Keywords

river chief system; text clustering; Linear discriminant analysis

Abstract

The river chief system is an innovation of China’s water resources management system, the core of which is to control and protect the water environment of rivers and lakes. The optimization of the river chief system cannot be achieved without the support and guarantee of policies. In this paper, the author adopts big data text clustering methods, such as the Linear discriminant analysis (LDA) model, to collect the topics of China’s river chief system policies and identify the policy texts’ potential topic information, intensity, and structural features. It is found that the topic of China’s river chief system policy has low topic similarity and little difference in topic intensity. In addition, policies enacted focus more on law enforcement supervision, ecological governance, performance appraisal, information management, and other fields, while policies on public participation and watershed coordination are insufficiently supplied. In the future, policy supply in this respect should be strengthened.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Zhimo Zhao
PY  - 2022
DA  - 2022/12/27
TI  - Textual Clustering Analysis of River Chief System Policy in China
BT  - Proceedings of the 2022 3rd International Conference on Big Data and Social Sciences (ICBDSS 2022)
PB  - Atlantis Press
SP  - 151
EP  - 160
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-064-0_17
DO  - 10.2991/978-94-6463-064-0_17
ID  - Zhao2022
ER  -